Flood Forecasting with Classical and Quantum Machine Learning Models: A Comparative Analysis

Flood forecasting is one of the most disastrous natural phenomena; however, its prediction with sufficient accuracy can help to at least reduce the damage caused by this potentially disastrous event. Additionally, in this paper, a comparison of classical and quantum models for machine learning in flood forecasting is made. Traditional machine learning means have found wide use in view of this, particularly when there exist historical data and a number of environmental parameters. But with the strength that quantum computing holds, quantum machine learning models are poised for even better predictive accuracy and efficiency for the future, as they have the capability to process loads of data all at once. In this text, we implement both the classical models, such as the Support Vector Machine (SVM) and Random Forests, alongside quantum machine learning models. We compare on prediction accuracy, computational efficiency, and scalability. The results will point out the strengths and limitations of each approach, from which it will be possible to derive how quantum machine learning can complement or surpass the performances of classical methods for the improved reliability and timeliness of predictions in flood forecasting.